A modified electromagnetic-bandgap (M-EBG) structure and its application to planar monopole ultra-wideband (UWB) antenna are presented. The proposed M-EBG which comprises two strip patch and an edge-located via can perform dual notched bands. By properly designing and placing strip patch near the feedline, the proposed M-EBG not only possesses a simple structure and compact size but also exhibits good band rejection. Moreover, it is easy to tune the dual notched bands by altering the dimensions of the M-EBG. A demonstration antenna with dual band-notched characteristics is designed and fabricated to validate the proposed method. The results show that the proposed antenna can satisfy the requirements of VSWR < 2 over UWB 3.1–10.6 GHz, except for the rejected bands of the world interoperability for microwave access (WiMAX) and the wireless local area network (WLAN) at 3.5 GHz and 5.5 GHz, respectively.
In this paper, we present a new dutycycle media access control(MAC) protocol, called Long-chain MAC protocol (LC-MAC), that exploits a mechanism for relay nodes booking in advance and transmitting in a burst manner in order to reduce the end-to-end delivery delay in a long-chain sensor network scenario without sacrificing energy efficiency. In LC-MAC, a super SYNC frame can travel across multiple hops and schedule the upcoming data packet delivery along the long-chain route. Each relay node sleeps and intelligently wakes up at a scheduled time, so that the upstream node can send the data packet to it and then it can immediately forwards the data packet to the downstream node. Our simulation results in ns-2 show that LC-MAC achieves significant improvement in end-to-end delivery delay over S-MAC without sacrificing energy efficiency or network throughput.
Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers' perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the carfollowing behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers' perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers' perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers' perceived risk is low due to the difficulties in vehicle operation and limited visibility.
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